SOC Working Group V. Hilt
Internet-Draft Bell Labs/Alcatel-Lucent
Intended status: Informational E. Noel
Expires: November 29, 2010 AT&T Labs
C. Shen
Columbia University
A. Abdelal
Sonus Networks
May 28, 2010
Design Considerations for Session Initiation Protocol (SIP) Overload
Control
draft-hilt-soc-overload-design-00
Abstract
Overload occurs in Session Initiation Protocol (SIP) networks when
SIP servers have insufficient resources to handle all SIP messages
they receive. Even though the SIP protocol provides a limited
overload control mechanism through its 503 (Service Unavailable)
response code, SIP servers are still vulnerable to overload. This
document discusses models and design considerations for a SIP
overload control mechanism.
Status of this Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 3
2. SIP Overload Problem . . . . . . . . . . . . . . . . . . . . . 4
3. Explicit vs. Implicit Overload Control . . . . . . . . . . . . 5
4. System Model . . . . . . . . . . . . . . . . . . . . . . . . . 5
5. Degree of Cooperation . . . . . . . . . . . . . . . . . . . . 7
5.1. Hop-by-Hop . . . . . . . . . . . . . . . . . . . . . . . . 8
5.2. End-to-End . . . . . . . . . . . . . . . . . . . . . . . . 9
5.3. Local Overload Control . . . . . . . . . . . . . . . . . . 10
6. Topologies . . . . . . . . . . . . . . . . . . . . . . . . . . 10
7. Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
8. Performance Metrics . . . . . . . . . . . . . . . . . . . . . 13
9. Explicit Overload Control Feedback . . . . . . . . . . . . . . 14
9.1. Rate-based Overload Control . . . . . . . . . . . . . . . 14
9.2. Loss-based Overload Control . . . . . . . . . . . . . . . 15
9.3. Window-based Overload Control . . . . . . . . . . . . . . 16
9.4. Overload Signal-based Overload Control . . . . . . . . . . 17
9.5. On-/Off Overload Control . . . . . . . . . . . . . . . . . 18
10. Implicit Overload Control . . . . . . . . . . . . . . . . . . 18
11. Overload Control Algorithms . . . . . . . . . . . . . . . . . 18
12. Message Prioritization . . . . . . . . . . . . . . . . . . . . 19
13. Security Considerations . . . . . . . . . . . . . . . . . . . 19
14. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
15. Informative References . . . . . . . . . . . . . . . . . . . . 19
Appendix A. Contributors . . . . . . . . . . . . . . . . . . . . 20
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 20
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1. Introduction
As with any network element, a Session Initiation Protocol (SIP)
[RFC3261] server can suffer from overload when the number of SIP
messages it receives exceeds the number of messages it can process.
Overload occurs if a SIP server does not have sufficient resources to
process all incoming SIP messages. These resources may include CPU,
memory, network bandwidth, input/output, or disk resources.
Overload can pose a serious problem for a network of SIP servers.
During periods of overload, the throughput of a network of SIP
servers can be significantly degraded. In fact, overload may lead to
a situation in which the throughput drops down to a small fraction of
the original processing capacity. This is often called congestion
collapse.
An overload control mechanism enables a SIP server to perform close
to its capacity limit during times of overload. Overload control is
used by a SIP server if it is unable to process all SIP requests due
to resource constraints. There are other failure cases in which a
SIP server can successfully process incoming requests but has to
reject them for other reasons. For example, a PSTN gateway that runs
out of trunk lines but still has plenty of capacity to process SIP
messages should reject incoming INVITEs using a 488 (Not Acceptable
Here) response [RFC4412]. Similarly, a SIP registrar that has lost
connectivity to its registration database but is still capable of
processing SIP messages should reject REGISTER requests with a 500
(Server Error) response [RFC3261]. Overload control mechanisms do
not apply in these cases and SIP provides appropriate response codes
for them.
The SIP protocol provides a limited mechanism for overload control
through its 503 (Service Unavailable) response code and the Retry-
After header. However, this mechanism cannot prevent overload of a
SIP server and it cannot prevent congestion collapse. In fact, it
may cause traffic to oscillate and to shift between SIP servers and
thereby worsen an overload condition. A detailed discussion of the
SIP overload problem, the problems with the 503 (Service Unavailable)
response code and the Retry-After header and the requirements for a
SIP overload control mechanism can be found in [RFC5390].
This document discusses the models, assumptions and design
considerations for a SIP overload control mechanism. The document is
a product of the SIP overload control design team.
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2. SIP Overload Problem
A key contributor to the SIP congestion collapse [RFC5390] is the
regenerative behavior of overload in the SIP protocol. When SIP is
running over the UDP protocol, it will retransmit messages that were
dropped by a SIP server due to overload and thereby increase the
offered load for the already overloaded server. This increase in
load worsens the severity of the overload condition and, in turn,
causes more messages to be dropped. A congestion collapse can occur
[Noel et al.], [Shen et al.] and [Hilt et al.].
Regenerative behavior under overload should ideally be avoided by any
protocol as this would lead to stable operation under overload.
However, this is often difficult to achieve in practice. For
example, changing the SIP retransmission timer mechanisms can reduce
the degree of regeneration during overload but will impact the
ability of SIP to recover from message losses. Without any
retransmission each message that is dropped due to SIP server
overload will eventually lead to a failed call.
For a SIP INVITE transaction to be successful a minimum of three
messages need to be forwarded by a SIP server. Often an INVITE
transaction consists of five or more SIP messages. If a SIP server
under overload randomly discards messages without evaluating them,
the chances that all messages belonging to a transaction are
successfully forwarded will decrease as the load increases. Thus,
the number of transactions that complete successfully will decrease
even if the message throughput of a server remains up and assuming
the overload behavior is fully non-regenerative. A SIP server might
(partially) parse incoming messages to determine if it is a new
request or a message belonging to an existing transaction. However,
after having spend resources on parsing a SIP message, discarding
this message is expensive as the resources already spend are lost.
The number of successful transactions will therefore decline with an
increase in load as less and less resources can be spent on
forwarding messages and more and more resources are consumed by
inspecting messages that will eventually be dropped. The slope of
the decline depends on the amount of resources spent to inspect each
message.
Another challenge for SIP overload control is that the rate of the
true traffic source usually cannot be controlled. Overload is often
caused by a large number of UAs each of which creates only a single
message. These UAs cannot be rate controlled as they only send one
message. However, the sum of their traffic can overload a SIP
server.
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3. Explicit vs. Implicit Overload Control
The main differences between explicit and implicit overload control
is the way overload is signaled from a SIP server that is reaching
overload condition to its upstream neighbors.
In an explicit overload control mechanism, a SIP server uses an
explicit overload signal to indicate that it is reaching its capacity
limit. Upstream neighbors receiving this signal can adjust their
transmission rate as indicated by the overload signal to a level that
is acceptable to the downstream server. The overload signal enables
a SIP server to steer the load it is receiving to a rate at which it
can perform at maximum capacity.
Implicit overload control uses the absence of responses and packet
loss as an indication of overload. A SIP server that is sensing such
a condition reduces the load it is forwarding a downstream neighbor.
Since there is no explicit overload signal, this mechanism is robust
as it does not depend on actions taken by the SIP server running into
overload.
The ideas of explicit and implicit overload control are in fact
complementary. By considering implicit overload indications a server
can avoid overloading an unresponsive downstream neighbor. An
explicit overload signal enables a SIP server to actively steer the
incoming load to a desired level.
4. System Model
The model shown in Figure 1 identifies fundamental components of an
explicit SIP overload control mechanism:
SIP Processor: The SIP Processor processes SIP messages and is the
component that is protected by overload control.
Monitor: The Monitor measures the current load of the SIP processor
on the receiving entity. It implements the mechanisms needed to
determine the current usage of resources relevant for the SIP
processor and reports load samples (S) to the Control Function.
Control Function: The Control Function implements the overload
control algorithm. The control function uses the load samples (S)
and determines if overload has occurred and a throttle (T) needs
to be set to adjust the load sent to the SIP processor on the
receiving entity. The control function on the receiving entity
sends load feedback (F) to the sending entity.
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Actuator: The Actuator implements the algorithms needed to act on
the throttles (T) and ensures that the amount of traffic forwarded
to the receiving entity meets the criteria of the throttle. For
example, a throttle may instruct the Actuator to not forward more
than 100 INVITE messages per second. The Actuator implements the
algorithms to achieve this objective, e.g., using message gapping.
It also implements algorithms to select the messages that will be
affected and determine whether they are rejected or redirected.
The type of feedback (F) conveyed from the receiving to the sending
entity depends on the overload control method used (i.e., loss-based,
rate-based, window-based or signal-based overload control; see
Section 9), the overload control algorithm (see Section 11) as well
as other design parameters. The feedback (F) enables the sending
entity to adjust the amount of traffic forwarded to the receiving
entity to a level that is acceptable to the receiving entity without
causing overload.
Figure 1 depicts a general system model for overload control. In
this diagram, one instance of the control function is on the sending
entity (i.e., associated with the actuator) and one is on the
receiving entity (i.e., associated with the monitor). However, a
specific mechanism may not require both elements. In this case, one
of two control function elements can be empty and simply passes along
feedback. E.g., if (F) is defined as a loss-rate (e.g., reduce
traffic by 10%) there is no need for a control function on the
sending entity as the content of (F) can be copied directly into (T).
The model in Figure 1 shows a scenario with one sending and one
receiving entity. In a more realistic scenario a receiving entity
will receive traffic from multiple sending entities and vice versa
(see Section 6). The feedback generated by a Monitor will therefore
often be distributed across multiple Actuators. A Monitor needs to
be able to split the load it can process across multiple sending
entities and generate feedback that correctly adjusts the load each
sending entity is allowed to send. Similarly, an Actuator needs to
be prepared to receive different levels of feedback from different
receiving entities and throttle traffic to these entities
accordingly.
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Sending Receiving
Entity Entity
+----------------+ +----------------+
| Server A | | Server B |
| +----------+ | | +----------+ | -+
| | Control | | F | | Control | | |
| | Function |<-+------+--| Function | | |
| +----------+ | | +----------+ | |
| T | | | ^ | | Overload
| v | | | S | | Control
| +----------+ | | +----------+ | |
| | Actuator | | | | Monitor | | |
| +----------+ | | +----------+ | |
| | | | ^ | -+
| v | | | | -+
| +----------+ | | +----------+ | |
<-+--| SIP | | | | SIP | | | SIP
--+->|Processor |--+------+->|Processor |--+-> | System
| +----------+ | | +----------+ | |
+----------------+ +----------------+ -+
Figure 1: System Model for Explicit Overload Control
5. Degree of Cooperation
A SIP request is usually processed by more than one SIP server on its
path to the destination. Thus, a design choice for an explicit
overload control mechanism is where to place the components of
overload control along the path of a request and, in particular,
where to place the Monitor and Actuator. This design choice
determines the degree of cooperation between the SIP servers on the
path. Overload control can be implemented hop-by-hop with the
Monitor on one server and the Actuator on its direct upstream
neighbor. Overload control can be implemented end-to-end with
Monitors on all SIP servers along the path of a request and an
Actuator on the sender. In this case, the Control Functions
associated with each Monitor have to cooperate to jointly determine
the overall feedback for this path. Finally, overload control can be
implemented locally on a SIP server if Monitor and Actuator reside on
the same server. In this case, the sending entity and receiving
entity are the same SIP server and Actuator and Monitor operate on
the same SIP processor (although, the Actuator typically operates on
a pre-processing stage in local overload control). Local overload
control is an internal overload control mechanism as the control loop
is implemented internally on one server. Hop-by-hop and end-to-end
are external overload control mechanisms. All three configurations
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are shown in Figure 2.
+---------+ +------(+)---------+
+------+ | | | ^ |
| | | +---+ | | +---+
v | v //=>| C | v | //=>| C |
+---+ +---+ // +---+ +---+ +---+ // +---+
| A |===>| B | | A |===>| B |
+---+ +---+ \\ +---+ +---+ +---+ \\ +---+
^ \\=>| D | ^ | \\=>| D |
| +---+ | | +---+
| | | v |
+---------+ +------(+)---------+
(a) hop-by-hop (b) end-to-end
+-+
v |
+-+ +-+ +---+
v | v | //=>| C |
+---+ +---+ // +---+
| A |===>| B |
+---+ +---+ \\ +---+
\\=>| D |
+---+
^ |
+-+
(c) local
==> SIP request flow
<-- Overload feedback loop
Figure 2: Degree of Cooperation between Servers
5.1. Hop-by-Hop
The idea of hop-by-hop overload control is to instantiate a separate
control loop between all neighboring SIP servers that directly
exchange traffic. I.e., the Actuator is located on the SIP server
that is the direct upstream neighbor of the SIP server that has the
corresponding Monitor. Each control loop between two servers is
completely independent of the control loop between other servers
further up- or downstream. In the example in Figure 2(a), three
independent overload control loops are instantiated: A - B, B - C and
B - D. Each loop only controls a single hop. Overload feedback
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received from a downstream neighbor is not forwarded further
upstream. Instead, a SIP server acts on this feedback, for example,
by rejecting SIP messages if needed. If the upstream neighbor of a
server also becomes overloaded, it will report this problem to its
upstream neighbors, which again take action based on the reported
feedback. Thus, in hop-by-hop overload control, overload is always
resolved by the direct upstream neighbors of the overloaded server
without the need to involve entities that are located multiple SIP
hops away.
Hop-by-hop overload control reduces the impact of overload on a SIP
network and can avoid congestion collapse. It is simple and scales
well to networks with many SIP entities. An advantage is that it
does not require feedback to be transmitted across multiple-hops,
possibly crossing multiple trust domains. Feedback is sent to the
next hop only. Furthermore, it does not require a SIP entity to
aggregate a large number of overload status values or keep track of
the overload status of SIP servers it is not communicating with.
5.2. End-to-End
End-to-end overload control implements an overload control loop along
the entire path of a SIP request, from UAC to UAS. An end-to-end
overload control mechanism consolidates overload information from all
SIP servers on the way (including all proxies and the UAS) and uses
this information to throttle traffic as far upstream as possible. An
end-to-end overload control mechanism has to be able to frequently
collect the overload status of all servers on the potential path(s)
to a destination and combine this data into meaningful overload
feedback.
A UA or SIP server only throttles requests if it knows that these
requests will eventually be forwarded to an overloaded server. For
example, if D is overloaded in Figure 2(b), A should only throttle
requests it forwards to B when it knows that they will be forwarded
to D. It should not throttle requests that will eventually be
forwarded to C, since server C is not overloaded. In many cases, it
is difficult for A to determine which requests will be routed to C
and D since this depends on the local routing decision made by B.
These routing decisions can be highly variable and, for example,
depend on call routing policies configured by the user, services
invoked on a call, load balancing policies, etc. The fact that a
previous message to a target has been routed through an overloaded
server does not necessarily mean the next message to this target will
also be routed through the same server.
The main problem of end-to-end overload control is its inherent
complexity since UAC or SIP servers need to monitor all potential
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paths to a destination in order to determine which requests should be
throttled and which requests may be sent. Even if this information
is available, it is not clear which path a specific request will
take.
A variant of end-to-end overload control is to implement a control
loop between a set of well-known SIP servers along the path of a SIP
request. For example, an overload control loop can be instantiated
between a server that only has one downstream neighbor or a set of
closely coupled SIP servers. A control loop spanning multiple hops
can be used if the sending entity has full knowledge about the SIP
servers on the path of a SIP message.
A key difference to transport protocols using end-to-end congestion
control such as TCP is that the traffic exchanged between SIP servers
consists of many individual SIP messages. Each of these SIP messages
has its own source and destination. Even SIP messages containing
identical SIP URIs (e.g., a SUBSCRIBE and a INVITE message to the
same SIP URI) can be routed to different destinations. This is
different from TCP which controls a stream of packets between a
single source and a single destination.
5.3. Local Overload Control
The idea of local overload control (see Figure 2(c)) is to run the
Monitor and Actuator on the same server. This enables the server to
monitor the current resource usage and to reject messages that can't
be processed without overusing the local resources. The fundamental
assumption behind local overload control is that it is less resource
consuming for a server to reject messages than to process them. A
server can therefore reject the excess messages it cannot process to
stop all retransmissions of these messages.
Local overload control can be used in conjunction with an other
overload control mechanisms and provides an additional layer of
protection against overload. It is fully implemented within a SIP
server and does not require cooperation between servers. In general,
SIP servers should apply other overload control techniques to control
load before a local overload control mechanism is activated as a
mechanism of last resort.
6. Topologies
The following topologies describe four generic SIP server
configurations. These topologies illustrate specific challenges for
an overload control mechanism. An actual SIP server topology is
likely to consist of combinations of these generic scenarios.
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In the "load balancer" configuration shown in Figure 3(a) a set of
SIP servers (D, E and F) receives traffic from a single source A. A
load balancer is a typical example for such a configuration. In this
configuration, overload control needs to prevent server A (i.e., the
load balancer) from sending too much traffic to any of its downstream
neighbors D, E and F. If one of the downstream neighbors becomes
overloaded, A can direct traffic to the servers that still have
capacity. If one of the servers serves as a backup, it can be
activated once one of the primary servers reaches overload.
If A can reliably determine that D, E and F are its only downstream
neighbors and all of them are in overload, it may choose to report
overload upstream on behalf of D, E and F. However, if the set of
downstream neighbors is not fixed or only some of them are in
overload then A should not activate an overload control since A can
still forward the requests destined to non-overloaded downstream
neighbors. These requests would be throttled as well if A would use
overload control towards its upstream neighbors.
In the "multiple sources" configuration shown in Figure 3(b), a SIP
server D receives traffic from multiple upstream sources A, B and C.
Each of these sources can contribute a different amount of traffic,
which can vary over time. The set of active upstream neighbors of D
can change as servers may become inactive and previously inactive
servers may start contributing traffic to D.
If D becomes overloaded, it needs to generate feedback to reduce the
amount of traffic it receives from its upstream neighbors. D needs
to decide by how much each upstream neighbor should reduce traffic.
This decision can require the consideration of the amount of traffic
sent by each upstream neighbor and it may need to be re-adjusted as
the traffic contributed by each upstream neighbor varies over time.
Server D can use a local fairness policy to determine much traffic it
accepts from each upstream neighbor.
In many configurations, SIP servers form a "mesh" as shown in
Figure 3(c). Here, multiple upstream servers A, B and C forward
traffic to multiple alternative servers D and E. This configuration
is a combination of the "load balancer" and "multiple sources"
scenario.
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+---+ +---+
/->| D | | A |-\
/ +---+ +---+ \
/ \ +---+
+---+-/ +---+ +---+ \->| |
| A |------>| E | | B |------>| D |
+---+-\ +---+ +---+ /->| |
\ / +---+
\ +---+ +---+ /
\->| F | | C |-/
+---+ +---+
(a) load balancer (b) multiple sources
+---+
| A |---\ a--\
+---+-\ \---->+---+ \
\/----->| D | b--\ \--->+---+
+---+--/\ /-->+---+ \---->| |
| B | \/ c-------->| D |
+---+---\/\--->+---+ | |
/\---->| E | ... /--->+---+
+---+--/ /-->+---+ /
| C |-----/ z--/
+---+
(c) mesh (d) edge proxy
Figure 3: Topologies
Overload control that is based on reducing the number of messages a
sender is allowed to send is not suited for servers that receive
requests from a very large population of senders, each of which only
infrequently sends a request. This scenario is shown in Figure 3(d).
An edge proxy that is connected to many UAs is a typical example for
such a configuration.
Since each UA typically only contributes a few requests, which are
often related to the same call, it can't decrease its message rate to
resolve the overload. In such a configuration, a SIP server can
resort to local overload control by rejecting a percentage of the
requests it receives with 503 (Service Unavailable) responses. Since
there are many upstream neighbors that contribute to the overall
load, sending 503 (Service Unavailable) to a fraction of them can
gradually reduce load without entirely stopping all incoming traffic.
The Retry-After header can be used in 503 (Service Unavailable)
responses to ask UAs to wait a given number of seconds before trying
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the call again. Using 503 (Service Unavailable) towards individual
sources can, however, not prevent overload if a large number of users
places calls at the same time.
Note: The requirements of the "edge proxy" topology are different
than the ones of the other topologies, which may require a
different method for overload control.
7. Fairness
There are many different ways to define fairness between multiple
upstream neighbors of a SIP server. In the context of SIP server
overload, it is helpful to describe two categories of fairness: basic
fairness and customized fairness. With basic fairness a SIP server
treats all end users equally and ensures that each end user has the
same chance of reaching the destination server. With customized
fairness, the server allocates resources according to different
priorities. An example application of the basic fairness criteria is
the "Third caller receives free tickets" scenario, where each end
user should have an equal success probability in making calls through
an overloaded SIP server, regardless of which service provider he/she
is subscribed to. An example of customized fairness would be a
server which assigns different resource allocations to its upstream
neighbors (e.g., service providers) as defined in a service level
agreement (SLA).
8. Performance Metrics
The performance of an overload control mechanism can be measured
using different metrics.
A key performance indicator is the goodput of a SIP server under
overload. Ideally, a SIP server will be enabled to perform at its
capacity limit during periods of overload. E.g., if a SIP server has
a processing capacity of 140 INVITE transactions per second then an
overload control mechanism should enable it to process 140 INVITEs
per second even if the offered load is much higher. The delay
introduced by a SIP server is another important indicator. An
overload control mechanism should ensure that the delay encountered
by a SIP message is not increased significantly during periods of
overload.
Reactiveness and stability are other important performance
indicators. An overload control mechanism should quickly react to an
overload occurrence and ensure that a SIP server does not become
overloaded even during sudden peaks of load. Similarly, an overload
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control mechanism should quickly remove all throttles if the overload
disappears. Stability is another important criteria. An overload
control mechanism should not cause significant oscillations of load
on a SIP server. The performance of SIP overload control mechanisms
is discussed in [Noel et al.], [Shen et al.] and [Hilt et al.].
In addition to the above metrics, there are other indicators that are
relevant for the evaluation of an overload control mechanism:
Fairness: Which types of fairness does the overload control
mechanism implement?
Self-limiting: Is the overload control self-limiting if a SIP server
becomes unresponsive?
Changes in neighbor set: How does the mechanism adapt to a changing
set of sending entities?
Data points to monitor: Which and how many data points does an
overload control mechanism need to monitor?
9. Explicit Overload Control Feedback
Explicit overload control feedback enables a receiver to indicate how
much traffic it wants to receive. Explicit overload control
mechanisms can be differentiated based on the type of information
conveyed in the overload control feedback and whether the control
function is in the receiving or sending entity (receiver- vs. sender-
based overload control).
9.1. Rate-based Overload Control
The key idea of rate-based overload control is to limit the request
rate at which an upstream element is allowed to forward traffic to
the downstream neighbor. If overload occurs, a SIP server instructs
each upstream neighbor to send at most X requests per second. Each
upstream neighbor can be assigned a different rate cap.
An example algorithm for an Actuator in the sending entity is request
gapping. After transmitting a request to a downstream neighbor, a
server waits for 1/X seconds before it transmits the next request to
the same neighbor. Requests that arrive during the waiting period
are not forwarded and are either redirected, rejected or buffered.
The rate cap ensures that the number of requests received by a SIP
server never increases beyond the sum of all rate caps granted to
upstream neighbors. Rate-based overload control protects a SIP
server against overload even during load spikes assuming there are no
new upstream neighbors that start sending traffic. New upstream
neighbors need to be considered in all rate caps assigned to upstream
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neighbors. The overall rate cap of a SIP server is determined by an
overload control algorithm, e.g., based on system load.
Rate-based overload control requires a SIP server to assign a rate
cap to each of its upstream neighbors while it is activated.
Effectively, a server needs to assign a share of its overall capacity
to each upstream neighbor. A server needs to ensure that the sum of
all rate caps assigned to upstream neighbors is not higher than its
actual processing capacity. This requires a SIP server to keep track
of the set of upstream neighbors and to adjust the rate cap if a new
upstream neighbor appears or an existing neighbor stops transmitting.
For example, if the capacity of the server is X and this server is
receiving traffic from two upstream neighbors, it can assign a rate
of X/2 to each of them. If a third sender appears, the rate for each
sender is lowered to X/3. If the overall rate cap is too high, a
server may experience overload. If the cap is too low, the upstream
neighbors will reject requests even though they could be processed by
the server.
An approach for estimating a rate cap for each upstream neighbor is
using a fixed proportion of a control variable, X, where X is
initially equal to the capacity of the SIP server. The server then
increases or decreases X until the workload arrival rate matches the
actual server capacity. Usually, this will mean that the sum of the
rate caps sent out by the server (=X) exceeds its actual capacity,
but enables upstream neighbors who are not generating more than their
fair share of the work to be effectively unrestricted. In this
approach, the server only has to measure the aggregate arrival rate.
However, since the overall rate cap is usually higher than the actual
capacity, brief periods of overload may occur.
9.2. Loss-based Overload Control
A loss percentage enables a SIP server to ask an upstream neighbor to
reduce the number of requests it would normally forward to this
server by a percentage X. For example, a SIP server can ask an
upstream neighbor to reduce the number of requests this neighbor
would normally send by 10%. The upstream neighbor then redirects or
rejects X percent of the traffic that is destined for this server.
An algorithm for the sending entity to implement a loss percentage is
to draw a random number between 1 and 100 for each request to be
forwarded. The request is not forwarded to the server if the random
number is less than or equal to X.
An advantage of loss-based overload control is that, the receiving
entity does not need to track the set of upstream neighbors or the
request rate it receives from each upstream neighbor. It is
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sufficient to monitor the overall system utilization. To reduce
load, a server can ask its upstream neighbors to lower the traffic
forwarded by a certain percentage. The server calculates this
percentage by combining the loss percentage that is currently in use
(i.e., the loss percentage the upstream neighbors are currently using
when forwarding traffic), the current system utilization and the
desired system utilization. For example, if the server load
approaches 90% and the current loss percentage is set to a 50%
traffic reduction, then the server can decide to increase the loss
percentage to 55% in order to get to a system utilization of 80%.
Similarly, the server can lower the loss percentage if permitted by
the system utilization.
Loss-based overload control requires that the throttle percentage is
adjusted to the current overall number of requests received by the
server. This is particularly important if the number of requests
received fluctuates quickly. For example, if a SIP server sets a
throttle value of 10% at time t1 and the number of requests increases
by 20% between time t1 and t2 (t1<t2), then the server will see an
increase in traffic by 10% between time t1 and t2. This is even
though all upstream neighbors have reduced traffic by 10% as told.
Thus, percentage throttling requires an adjustment of the throttling
percentage in response to the traffic received and may not always be
able to prevent a server from encountering brief periods of overload
in extreme cases.
9.3. Window-based Overload Control
The key idea of window-based overload control is to allow an entity
to transmit a certain number of messages before it needs to receive a
confirmation for the messages in transit. Each sender maintains an
overload window that limits the number of messages that can be in
transit without being confirmed.
Each sender maintains an unconfirmed message counter for each
downstream neighbor it is communicating with. For each message sent
to the downstream neighbor, the counter is increased. For each
confirmation received, the counter is decreased. The sender stops
transmitting messages to the downstream neighbor when the unconfirmed
message counter has reached the current window size.
A crucial parameter for the performance of window-based overload
control is the window size. Each sender has an initial window size
it uses when first sending a request. This window size can be
changed based on the feedback it receives from the receiver.
The sender adjusts its window size as soon as it receives the
corresponding feedback from the receiver. If the new window size is
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smaller than the current unconfirmed message counter, the sender
stops transmitting messages until more messages are confirmed and the
current unconfirmed message counter is less than the window size.
Note that the reception of a 100 Trying response does not provide a
confirmation for the reception of a message. 100 Trying responses are
often created by a SIP server very early in processing and do not
indicate that a message has been successfully processed and cleared
from the input buffer. If the downstream neighbor is a stateless
proxy, it will not create 100 Trying responses at all and instead
pass through 100 Trying responses created by the next stateful
server. Also, 100 Trying responses are typically only created for
INVITE requests. Explicit message confirmations do not have these
problems.
Window-based overload control is similar to rate-based overload
control in that the total available receiver buffer space needs to be
divided among all upstream neighbors. However, unlike rate-based
overload control, window-based overload control is self-limiting and
can ensure that the receiver buffer does not overflow under normal
conditions. The transmission of messages by senders is clocked by
message confirmations received from the receiver. A buffer overflow
can occur if a large number of new upstream neighbors arrives at the
same time. However, senders will eventually stop transmitting new
requests once their initial sending window is closed.
In window-based overload control, the number of messages a sender is
allowed to send can frequently be set to zero. In this state, the
sender needs to be informed when it is allowed to send again and the
receiver window has opened up. However, since the sender is not
allowed to transmit messages, the receiver cannot convey the new
window size by piggybacking it in a response to another message.
Instead, it needs to inform the sender through another mechanism,
e.g., by sending a message that contains the new window size.
9.4. Overload Signal-based Overload Control
The key idea of overload signal-based overload control is to use the
transmission of a 503 (Service Unavailable) response as a signal for
overload in the downstream neighbor. After receiving a 503 (Service
Unavailable) response, the sender reduces the load forwarded to the
downstream neighbor to avoid triggering more 503 (Service
Unavailable) responses. The sender keeps reducing the load if more
503 (Service Unavailable) responses are received. Note that this
scheme is based on the use of 503 (Service Unavailable) responses
without Retry-After header as the Retry-After header would require a
sender to entirely stop forwarding requests.
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A sender which has not received 503 (Service Unavailable) responses
for a while but is still throttling traffic can start to increase the
offered load. By slowly increasing the traffic forwarded a sender
can detect that overload in the downstream neighbor has been resolved
and more load can be forwarded. The load is increased until the
sender again receives another 503 (Service Unavailable) response or
is forwarding all requests it has. A possible algorithm for
adjusting traffic is additive increase/multiplicative decrease
(AIMD).
Overload Signal-based Overload Control is a sender-based overload
control mechanism.
9.5. On-/Off Overload Control
On-/off overload control feedback enables a SIP server to turn the
traffic it is receiving either on or off. The 503 (Service
Unavailable) response with Retry-After header implements on-/off
overload control. On-/off overload control is less effective in
controlling load than the fine grained control methods above. In
fact, all above methods can realize on/-off overload control, e.g.,
by setting the allowed rate to either zero or unlimited.
10. Implicit Overload Control
Implicit overload control ensures that the transmission of a SIP
server is self-limiting. It slows down the transmission rate of a
sender when there is an indication that the receiving entity is
experiencing overload. Such an indication can be that the receiving
entity is not responding within the expected timeframe or is not
responding at all. The idea of implicit overload control is that
senders should try to sense overload of a downstream neighbor even if
there is no explicit overload control feedback. It avoids that an
overloaded server, which has become unable to generate overload
control feedback, will be overwhelmed with requests.
Window-based overload control is inherently self-limiting since a
sender cannot continue without receiving confirmations. All other
explicit overload control schemes described above do not have this
property and require additional implicit controls to limit
transmissions in case an overloaded downstream neighbor does not
generate explicit feedback.
11. Overload Control Algorithms
An important aspect of the design of an overload control mechanism is
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the overload control algorithm. The control algorithm determines
when the amount of traffic to a SIP server needs to be decreased and
when it can be increased. In terms of the model described in
Section 4 the control algorithm takes (S) as an input value and
generates (T) as a result.
Overload control algorithms have been studied to a large extent and
many different overload control algorithms exist. With many
different overload control algorithms available, it seems reasonable
to suggest a baseline algorithm in a specification for a SIP overload
control mechanism and allow the use of other algorithms if they
provide the same protocol semantics. This will also allow the
development of future algorithms, which may lead to a better
performance.
12. Message Prioritization
Overload control can require a SIP server to prioritize messages and
select messages that need to be rejected or redirected. The
selection is largely a matter of local policy of the SIP server. As
a general rule, a SIP server should preserve high-priority requests
such as emergency service requests as much as possible during times
of overload. It should also prioritize messages for ongoing sessions
over messages that set up a new session.
13. Security Considerations
Overload control mechanisms, in general, have security implications.
If not designed carefully they can, for example, be used to launch a
denial of service attack. The specific security risks and their
remedies depend on the actual protocol mechanisms chosen for overload
control. They need to be addressed in a document that specifies such
a mechanism.
14. IANA Considerations
This document does not require any IANA considerations.
15. Informative References
[Hilt et al.]
Hilt, V. and I. Widjaja, "Controlling Overload in Networks
of SIP Servers", IEEE International Conference on Network
Protocols (ICNP'08), Orlando, Florida, October 2008.
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[Noel et al.]
Noel, E. and C. Johnson, "Initial Simulation Results That
Analyze SIP Based VoIP Networks Under Overload",
International Teletraffic Congress (ITC'07), Ottawa,
Canada, June 2007.
[RFC3261] Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston,
A., Peterson, J., Sparks, R., Handley, M., and E.
Schooler, "SIP: Session Initiation Protocol", RFC 3261,
June 2002.
[RFC4412] Schulzrinne, H. and J. Polk, "Communications Resource
Priority for the Session Initiation Protocol (SIP)",
RFC 4412, February 2006.
[RFC5390] Rosenberg, J., "Requirements for Management of Overload in
the Session Initiation Protocol", RFC 5390, December 2008.
[Shen et al.]
Shen, C., Schulzrinne, H., and E. Nahum, "Session
Initiation Protocol (SIP) Server Overload Control: Design
and Evaluation, Principles", Systems and Applications of
IP Telecommunications (IPTComm'08), Heidelberg, Germany,
July 2008.
Appendix A. Contributors
Contributors to this document are: Mary Barnes (Nortel), Carolyn
Johnson (AT&T Labs), Daryl Malas (CableLabs), Tom Phelan (Sonus
Networks), Jonathan Rosenberg (Cisco), Henning Schulzrinne (Columbia
University), Nick Stewart (British Telecommunications plc), Rich
Terpstra (Level 3), Fangzhe Chang (Bell Labs/Alcatel-Lucent). Many
thanks!
Authors' Addresses
Volker Hilt
Bell Labs/Alcatel-Lucent
791 Holmdel-Keyport Rd
Holmdel, NJ 07733
USA
Email: volker.hilt@alcatel-lucent.com
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Eric Noel
AT&T Labs
Email: eric.noel@att.com
Charles Shen
Columbia University
Email: charles@cs.columbia.edu
Ahmed Abdelal
Sonus Networks
Email: aabdelal@sonusnet.com
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